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Online Monitoring of Surface Quality for Diagnostic Features in 3D Printing

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dc.contributor.author Lishchenko, Natalia
dc.contributor.author Ліщенко, Наталя Володимирівна
dc.contributor.author Pitel, Jan
dc.contributor.author Пітель, Ян
dc.contributor.author Larshin, Vasily
dc.contributor.author Ларшин, Василь Петрович
dc.date.accessioned 2022-10-28T06:56:11Z
dc.date.available 2022-10-28T06:56:11Z
dc.date.issued 2022
dc.identifier.citation Lishchenko, N.; Pitel’, J.; Larshin, V. Online Monitoring of Surface Quality for Diagnostic Features in 3D Printing. Machines 2022, 10, 541. https://doi.org/ 10.3390/machines10070541 en
dc.identifier.issn 2075-1702
dc.identifier.uri https://doi.org/ 10.3390/machines10070541
dc.identifier.uri http://dspace.opu.ua/jspui/handle/123456789/13029
dc.identifier.uri https://www.researchgate.net/publication/361763750_Online_Monitoring_of_Surface_Quality_for_Diagnostic_Features_in_3D_Printing
dc.description.abstract Investigation into non-destructive testing and evaluation of 3D printing quality is relevant due to the lack of reliable methods for non-destructive testing of 3D printing defects, including testing of the surface quality of 3D printed parts. The article shows how it is possible to increase the efficiency of online monitoring of the quality of the 3D printing technological process through the use of an optical contactless high-performance measuring instrument. A comparative study of contact (R130 roughness tester) and non-contact (LJ-8020 laser profiler) methods for determining the height of irregularities on the surface of a steel reference specimen was performed. It was found that, in the range of operation of the contact method (Ra 0.03–6.3 m and Rz 0.2–18.5 m), the errors of the contactless method in determining the standard surface roughness indicators Ra and Rz were 23.7% and 1.6%, respectively. Similar comparative studies of contact and non-contact methods were performed with three defect-free samples made of plastic polylactic acid (PLA), with surface irregularities within the specified range of operation of the contact method. The corresponding errors increased and amounted to 65.96% and 76.32%. Finally, investigations were carried out using only the non-contact method for samples with different types of 3D printing defects. It was found that the following power spectral density (PSD) estimates can be used as diagnostic features for determining 3D printing defects: Variance and Median. These generalized estimates are the most sensitive to 3D printing defects and can be used as diagnostic features in online monitoring of object surface quality in 3D printing. en
dc.language.iso en_US en
dc.publisher MDPI en
dc.subject signal processing en
dc.subject frequency analysis en
dc.subject monitoring system en
dc.subject vision-based method en
dc.subject laser profiler en
dc.subject non-contact method en
dc.subject surface roughness en
dc.subject quality assessment en
dc.title Online Monitoring of Surface Quality for Diagnostic Features in 3D Printing en
dc.type Article in Scopus en
opu.kafedra Кафедра цифрових технологій в інжинірингу
opu.citation.journal Machines en
opu.citation.volume 10 en
opu.citation.issue 7 en
opu.staff.id larshin.v.p@op.edu.ua en


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